Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

IAPR Workshop on Artificial Neural Networks in Pattern Recognition

ANNPR 2012: Artificial Neural Networks in Pattern Recognition pp 14–23Cite as

  1. Home
  2. Artificial Neural Networks in Pattern Recognition
  3. Conference paper
Kernel Robust Soft Learning Vector Quantization

Kernel Robust Soft Learning Vector Quantization

  • Daniela Hofmann22 &
  • Barbara Hammer22 
  • Conference paper
  • 1395 Accesses

  • 6 Citations

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7477)

Abstract

Prototype-based classification schemes offer very intuitive and flexible classifiers with the benefit of easy interpretability of the results and scalability of the model complexity. Recent prototype-based models such as robust soft learning vector quantization (RSLVQ) have the benefit of a solid mathematical foundation of the learning rule and decision boundaries in terms of probabilistic models and corresponding likelihood optimization. In its original form, they can be used for standard Euclidean vectors only. In this contribution, we extend RSLVQ towards a kernelized version which can be used for any positive semidefinite data matrix. We demonstrate the superior performance of the technique, kernel RSLVQ, in a variety of benchmarks where results competitive or even superior to state-of-the-art support vector machines are obtained.

Keywords

  • Support Vector Machine
  • Learning Rule
  • Vector Quantization
  • Learn Vector Quantization
  • Learn Vector Quantization Network

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Download conference paper PDF

References

  1. Biehl, M., Ghosh, A., Hammer, B.: Dynamics and generalization ability of LVQ algorithms. Journal of Machine Learning Research 8, 323–360 (2007)

    MathSciNet  MATH  Google Scholar 

  2. Biehl, M., Hammer, B., Verleysen, M., Villmann, T. (eds.): Similarity-Based Clustering. LNCS (LNAI), vol. 5400. Springer, Heidelberg (2009)

    Google Scholar 

  3. Boulet, R., Jouve, B., Rossi, F., Villa, N.: Batch kernel SOM and related Laplacian methods for social network analysis. Neurocomputing 71(7-9), 1257–1273 (2008)

    CrossRef  Google Scholar 

  4. Chen, Y., Garcia, E.K., Gupta, M.R., Rahimi, A., Cazzanti, L.: Similarity-based classification: Concepts and algorithms. JMLR 10, 747–776 (2009)

    MathSciNet  MATH  Google Scholar 

  5. Cottrell, M., Hammer, B., Hasenfuss, A., Villmann, T.: Batch and median neural gas. Neural Networks 19, 762–771 (2006)

    CrossRef  MATH  Google Scholar 

  6. Frasconi, P., Gori, M., Sperduti, A.: A general framework for adaptive processing of data structures. IEEE TNN 9(5), 768–786 (1998)

    Google Scholar 

  7. Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)

    CrossRef  MathSciNet  MATH  Google Scholar 

  8. Gärtner, T.: Kernels for Structured Data. PhD thesis, Univ. Bonn (2005)

    Google Scholar 

  9. Hammer, B., Hasenfuss, A.: Topographic mapping of large dissimilarity datasets. Neural Computation 22(9), 2229–2284 (2010)

    CrossRef  MathSciNet  MATH  Google Scholar 

  10. Hammer, B., Micheli, A., Sperduti, A.: Universal approximation capability of cascade correlation for structures. Neural Computation 17, 1109–1159 (2005)

    CrossRef  MathSciNet  MATH  Google Scholar 

  11. Hammer, B., Villmann, T.: Generalized relevance learning vector quantization. Neural Networks 15(8-9), 1059–1068 (2002)

    CrossRef  Google Scholar 

  12. Kohonen, T.: Self-Oganizing Maps, 3rd edn. Springer (2000)

    Google Scholar 

  13. Kohonen, T., Somervuo, P.: How to make large self-organizing maps for nonvectorial data. Neural Networks 15(8-9), 945–952 (2002)

    CrossRef  Google Scholar 

  14. Pekalska, E., Duin, R.P.: The Dissimilarity Representation for Pattern Recognition. Foundations and Applications. World Scientific (2005)

    Google Scholar 

  15. Qin, A.K., Suganthan, P.N.: Kernel neural gas algorithms with application to cluster analysis. In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR 2004), vol. 4, pp. 617–620. IEEE Computer Society, Washington, DC (2004)

    CrossRef  Google Scholar 

  16. Qin, A.K., Suganthan, P.N.: A novel kernel prototype-based learning algorithm. In: Proc. of the 17th International Conference on Pattern Recognition (ICPR 2004), Cambridge, UK (August 2004)

    Google Scholar 

  17. Rossi, F., Villa-Vialaneix, N.: Consistency of functional learning methods based on derivatives. Pat. Rec. Letters 32(8), 1197–1209 (2011)

    CrossRef  Google Scholar 

  18. Sato, A., Yamada, K.: Generalized Learning Vector Quantization. In: NIPS (1995)

    Google Scholar 

  19. Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: Computational capabilities of graph neural networks. IEEE TNN 20(1), 81–102 (2009)

    Google Scholar 

  20. Schneider, P., Biehl, M., Hammer, B.: Distance learning in discriminative vector quantization. Neural Computation 21, 2942–2969 (2009)

    CrossRef  MathSciNet  MATH  Google Scholar 

  21. Seo, S., Obermayer, K.: Soft learning vector quantization. Neural Comput. 15, 1589–1604 (2003)

    CrossRef  MATH  Google Scholar 

  22. van der Maaten, L., Hinton, G.: Visualizing high-dimensional data using t-sne. JMLR 9, 2579–2605 (2008)

    MATH  Google Scholar 

  23. Vellido, A., Martin-Guerroro, J.D., Lisboa, P.: Making machine learning models interpretable. In: ESANN 2012 (2012)

    Google Scholar 

  24. Williams, C., Seeger, M.: Using the nyström method to speed up kernel machines. In: Advances in Neural Information Processing Systems 13, pp. 682–688. MIT Press (2001)

    Google Scholar 

  25. Zhu, X., Gisbrecht, A., Schleif, F.-M., Hammer, B.: Approximation techniques for clustering dissimilarity data. Neurocomputing 90, 72–84 (2012)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CITEC Center of Excellence, Bielefeld University, Germany

    Daniela Hofmann & Barbara Hammer

Authors
  1. Daniela Hofmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Barbara Hammer
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Fondazione Bruno Kessler (FBK), 38123, Trento, Italy

    Nadia Mana

  2. Institute of Neural Information Processing, University of Ulm, 89069, Ulm, Germany

    Friedhelm Schwenker

  3. Dipartimento di Ingegneria dell’Informazione, Università di Siena, 53100, Siena, Italy

    Edmondo Trentin

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hofmann, D., Hammer, B. (2012). Kernel Robust Soft Learning Vector Quantization. In: Mana, N., Schwenker, F., Trentin, E. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2012. Lecture Notes in Computer Science(), vol 7477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33212-8_2

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-33212-8_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33211-1

  • Online ISBN: 978-3-642-33212-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • The International Association for Pattern Recognition

    Published in cooperation with

    http://www.iapr.org/

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature